Lifting GIS Maps into Strong Geometric Context for Scene Understanding

Contextual information can have a substantial impact on the performance of visual tasks such as semantic segmentation, object detection, and geometric estimation. Data stored in Geographic Information Systems (GIS) offers a rich source of contextual information that has been largely untapped by computer vision. We propose to leverage such information for scene understanding by combining GIS resources with large sets of unorganized photographs using Structure from Motion (SfM) techniques. We present a pipeline to quickly generate strong 3D geometric priors from 2D GIS data using SfM models aligned with minimal user input. Given an image resectioned against this model, we generate robust predictions of depth, surface normals, and semantic labels. We show that the precision of the predicted geometry is substantially more accurate other single-image depth estimation methods. We then demonstrate the utility of these contextual constraints for re-scoring pedestrian detections, and use these GIS contextual features alongside object detection score maps to improve a CRF-based semantic segmentation framework, boosting accuracy over baseline models

Bootstrap methods for the empirical study of decision-making and information flows in social systems

Abstract: We characterize the statistical bootstrap for the estimation of information theoretic quantities from data, with particular reference to its use in the study of large-scale social phenomena. Our methods allow one to preserve, approximately, the underlying axiomatic relationships of information theory—in particular, consistency under arbitrary coarse-graining—that motivate use of these quantities in the first place, while providing reliability comparable to the state of the art for Bayesian estimators. We show how information-theoretic quantities allow for rigorous empirical study of the decision-making capacities of rational agents, and the time-asymmetric flows of information in distributed systems. We provide illustrative examples by reference to ongoing collaborative work on the semantic structure of the British Criminal Court system and the conflict dynamics of the contemporary Afghanistan insurgency

Some Ontological Principles for Designing Upper Level Lexical Resources

The purpose of this paper is to explore some semantic problems related to the use of linguistic ontologies in information systems, and to suggest some organizing principles aimed to solve such problems. The taxonomic structure of current ontologies is unfortunately quite complicated and hard to understand, especially for what concerns the upper levels. I will focus here on the problem of ISA overloading, which I believe is the main responsible of these difficulties. To this purpose, I will carefully analyze the ontological nature of the categories used in current upper-level structures, considering the necessity of splitting them according to more subtle distinctions or the opportunity of excluding them because of their limited organizational role.Comment: 8 pages - gzipped postscript file - A4 forma

Investigating the Application of Semantic web layers in management systems of Iranian Journals

The aim of this study was to investigate the structure and presence of semantic web layers in the journal management systems of the country, which is applied in terms of purpose and in terms of data collection, has been done by survey method and descriptive approach. The statistical population of the study constitutes all active systems of Iranian journals. In this study, a checklist was used to collect data and data analysis was performed using SPSS software. Studies on the structure of the semantic technology architecture of journal management systems in the field of information retrieval showed that the neo-scriber system (Kowsar) has gained the first rank among the 5 systems studied; And Semina and Yektaweb systems were in the second place. It was also observed that among the semantic web layers, the URI layer had the highest presence and the RDF layer had the lowest presence in journal management systems. Chi-square test results showed that there was no significant difference between the presence of semantic web layers in different journal management systems. It seems that the use of semantic web in information storage aretrieval systems in the information society is an important issue that should be paid more attention than before and the necessary infrastructure to use these technologies Created. Journal management systems that have taken good measures in this regard so far need to do their best to use this technology in order to provide decent services to the scientific community

Language technology for eLearning

Given the huge amount of static and dynamic contents created for eLearning tasks, the major challenge for their wide use is to improve their retrieval and accessibility within Learning Management Systems. This paper describes the LT4eL project which addresses this challenge by proposing Language Technology based functionalities to support semi-automatic metadata generation for the description of the learning objects, on the basis of a linguistic analysis of the content. Se- mantic knowledge will be integrated to enhance the management, distribution and retrieval of the learning material. We will employ ontologies, key elements in the architecture of the Semantic Web initiative, to structure the learning material within Learning Management Systems, by means of the descriptive metadata. We will also explore the use of Latent Semantic Indexing techniques for the matching of the learning objects with the user information requirements.peer-reviewe